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Twitter: A window on alcohol use?
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Abstracts / Drug and Alcohol Dependence 140 (2014) e169–e251
Methods: During each of the 4 outpatient sessions, nontreatment seeking, daily MJ smokers (N = 17M, 1F) smoked six 5-s puffs from 3 MJ cigarettes (2 puffs from each cigarette). The number of puffs from active (≥5.5% THC) and inactive (0.0% THC) MJ cigarettes smoked during each session varied according to condition (0, 2, 4, or 6 active puffs); active puffs smoked before placebo puffs. The subjective, physiological and psychomotor-task performance effects of MJ as a function of puff condition (‘dose’) were assessed at timed intervals prior to and after MJ administration. Results: Active MJ dose-dependently increased heart rate and decreased carbon monoxide expiration (p ≤ 0.001), an index of inhalation strength. Active MJ also increased subjective ratings of marijuana ‘Strength,’ ‘High,’ and positive subjective-effect ratings such as ‘Liking,’ ‘Good Effect’ and ‘Take Again’ (p ≤ 0.001) compared to inactive MJ, but these effects were not dose-dependent. Active MJ also produced modest deficits in attention, psychomotor function and recall relative to inactive MJ. Conclusions: These findings demonstrate that the dosing procedure elicited dose-dependent cardiovascular effects even though participants inhaled less as MJ dose increased. Yet, most subjective ratings of marijuana intoxication did not vary as a function of dose. We hypothesize that participants’ expectation of MJ intoxication makes it difficult to detect dose-dependent differences in subjective drug effects. Thus, controlled investigations of MJ’s effects need to control for the likely contribution of expectancy effects. Financial support: Supported by NIDA Grant DA19239. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.509 Twitter: A window on alcohol use? K.A. Ramezani 1 , F. Terdal 1 , N. Pepper 2 , Kristen G. Anderson 1 1 Psychology, Reed, College, Portland, OR, United States 2 LuckySort, Portland, OR, United States
Aims: Recent innovations in social networking have generated new sources of information for behavioral scientists. The available literature on Twitter explores methodologies to mine Tweets for sentiment and how accurately that content predicts behavior, such as stock market trends or public safety (Bollen et al., 2010; Mao et al., 2011). Few studies have examined Twitter as a source of health-related information. Golder and Macy (2011) investigated the frequency of positive or negative affect tweets across the span of a day, week, month, and year and found that the Twitter data was consistent with the existing literature regarding mood and circadian rhythms. Our study explores Twitter’s application to public health concerns and aims to determine whether such data is a viable methodology for the behavioral sciences. Methods: Data consisted of approximately nine months of randomly sampled Twitter data, or tweets, from English-speaking users. The average age of Twitter users is 31 years old, with a majority of users falling in the 18–49 age range (Fox et al., 2009). TopicWatch, a Twitter analytics program developed by the company LuckySort, was used to examine the frequency of tweets regarding self-referent drinking behavior across time. Results: In a randomly selected day, peaks in drinking-related tweets occurred during 8:00 PM and 10:00 PM. Consistent with Goldman et al. (2011), the highest occurrences of self-referent tweets containing “drunk” in a given week were on Saturdays. Further, holidays and special events across this period showed spikes in self-referent tweets focusing on drunkenness, including SuperBowl Sunday, St. Patrick’s Day, Cinco de Mayo, July 4th, and Thanksgiving.
Conclusions: This investigation suggests that Twitter may be a viable methodology for examining public health phenomenon. These findings begin to reflect the potential efficacy of social networking platforms as a means of assessing large populations. The congruence of Twitter self-report data with empirically supported alcohol use patterns is encouraging in its implications for future prevention methods. Financial support: None. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.510 California smokin’: Tobacco and marijuana use among young adults in California compared to the rest of the U.S. Danielle Ramo 1 , H. Liu 1 , K. Chavez 1,2 , J.J. Prochaska 1,3 1
University of California San Francisco, San Francisco, CA, United States 2 Sonoma State University, Rohnert Park, CA, United States 3 Stanford University, Stanford, CA, United States Aims: Californians are more likely to smoke marijuana (MJ) and less likely to smoke tobacco (TOB) compared to most other states in the US. The current study examined young adults’ co-use of tobacco and marijuana and their thoughts about quitting. Methods: Young adults age 18–25 (N = 1987) across the US who had smoked at least one cigarette in the past month completed an online anonymous survey. More than half (53%) had used MJ in the past 30 days. Controlling for age, gender, ethnicity, household income, and urban/rural area, linear and logistic regressions compared 3 location groups on TOB and MJ variables: (1) California; (2) states with legal medical MJ programs (medMJ); and (3) states without a medical MJ program (no-medMJ). Results: Controlling for demographic differences, compared to medMJ and no-medMJ states, Californians used TOB less frequently and less intensely (21 days/mo vs. 23 for other 2 groups; p < .01; 3.8 cigs per day vs. 5.3 medMJ, 6.2 no-medMJ; p < .01; 57% daily smokers vs. 70% medMJ vs. 71% no-medMJ; p < .01). MJ use was more prevalent in California than other states (59% vs. 53% medMJ, 52% no-medMJ; p < .05). Compared to the other two groups, Californians had less positive expectancies regarding TOB use (p = .03) and anticipated greater success with quitting TOB (p < .01). There were no significant differences on TOB or MJ readiness to quit, % days using both TOB and MJ, abstinence goals, or thoughts about MJ abstinence. Conclusions: Young adult smokers living in California smoked TOB less frequently and intently and anticipated greater success with quitting smoking relative to other states. They were more likely to use MJ but rates of TOB/MJ co-use did not differ. Further, findings suggested comparable thoughts about MJ use and quitting among young adult smokers irrespective of the medical MJ policy in their state. Financial support: TRDRP; 18FT-0055; PI, D. Ramo. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.511
Abstracts / Drug and Alcohol Dependence 140 (2014) e169–e251
Methods: During each of the 4 outpatient sessions, nontreatment seeking, daily MJ smokers (N = 17M, 1F) smoked six 5-s puffs from 3 MJ cigarettes (2 puffs from each cigarette). The number of puffs from active (≥5.5% THC) and inactive (0.0% THC) MJ cigarettes smoked during each session varied according to condition (0, 2, 4, or 6 active puffs); active puffs smoked before placebo puffs. The subjective, physiological and psychomotor-task performance effects of MJ as a function of puff condition (‘dose’) were assessed at timed intervals prior to and after MJ administration. Results: Active MJ dose-dependently increased heart rate and decreased carbon monoxide expiration (p ≤ 0.001), an index of inhalation strength. Active MJ also increased subjective ratings of marijuana ‘Strength,’ ‘High,’ and positive subjective-effect ratings such as ‘Liking,’ ‘Good Effect’ and ‘Take Again’ (p ≤ 0.001) compared to inactive MJ, but these effects were not dose-dependent. Active MJ also produced modest deficits in attention, psychomotor function and recall relative to inactive MJ. Conclusions: These findings demonstrate that the dosing procedure elicited dose-dependent cardiovascular effects even though participants inhaled less as MJ dose increased. Yet, most subjective ratings of marijuana intoxication did not vary as a function of dose. We hypothesize that participants’ expectation of MJ intoxication makes it difficult to detect dose-dependent differences in subjective drug effects. Thus, controlled investigations of MJ’s effects need to control for the likely contribution of expectancy effects. Financial support: Supported by NIDA Grant DA19239. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.509 Twitter: A window on alcohol use? K.A. Ramezani 1 , F. Terdal 1 , N. Pepper 2 , Kristen G. Anderson 1 1 Psychology, Reed, College, Portland, OR, United States 2 LuckySort, Portland, OR, United States
Aims: Recent innovations in social networking have generated new sources of information for behavioral scientists. The available literature on Twitter explores methodologies to mine Tweets for sentiment and how accurately that content predicts behavior, such as stock market trends or public safety (Bollen et al., 2010; Mao et al., 2011). Few studies have examined Twitter as a source of health-related information. Golder and Macy (2011) investigated the frequency of positive or negative affect tweets across the span of a day, week, month, and year and found that the Twitter data was consistent with the existing literature regarding mood and circadian rhythms. Our study explores Twitter’s application to public health concerns and aims to determine whether such data is a viable methodology for the behavioral sciences. Methods: Data consisted of approximately nine months of randomly sampled Twitter data, or tweets, from English-speaking users. The average age of Twitter users is 31 years old, with a majority of users falling in the 18–49 age range (Fox et al., 2009). TopicWatch, a Twitter analytics program developed by the company LuckySort, was used to examine the frequency of tweets regarding self-referent drinking behavior across time. Results: In a randomly selected day, peaks in drinking-related tweets occurred during 8:00 PM and 10:00 PM. Consistent with Goldman et al. (2011), the highest occurrences of self-referent tweets containing “drunk” in a given week were on Saturdays. Further, holidays and special events across this period showed spikes in self-referent tweets focusing on drunkenness, including SuperBowl Sunday, St. Patrick’s Day, Cinco de Mayo, July 4th, and Thanksgiving.
Conclusions: This investigation suggests that Twitter may be a viable methodology for examining public health phenomenon. These findings begin to reflect the potential efficacy of social networking platforms as a means of assessing large populations. The congruence of Twitter self-report data with empirically supported alcohol use patterns is encouraging in its implications for future prevention methods. Financial support: None. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.510 California smokin’: Tobacco and marijuana use among young adults in California compared to the rest of the U.S. Danielle Ramo 1 , H. Liu 1 , K. Chavez 1,2 , J.J. Prochaska 1,3 1
University of California San Francisco, San Francisco, CA, United States 2 Sonoma State University, Rohnert Park, CA, United States 3 Stanford University, Stanford, CA, United States Aims: Californians are more likely to smoke marijuana (MJ) and less likely to smoke tobacco (TOB) compared to most other states in the US. The current study examined young adults’ co-use of tobacco and marijuana and their thoughts about quitting. Methods: Young adults age 18–25 (N = 1987) across the US who had smoked at least one cigarette in the past month completed an online anonymous survey. More than half (53%) had used MJ in the past 30 days. Controlling for age, gender, ethnicity, household income, and urban/rural area, linear and logistic regressions compared 3 location groups on TOB and MJ variables: (1) California; (2) states with legal medical MJ programs (medMJ); and (3) states without a medical MJ program (no-medMJ). Results: Controlling for demographic differences, compared to medMJ and no-medMJ states, Californians used TOB less frequently and less intensely (21 days/mo vs. 23 for other 2 groups; p < .01; 3.8 cigs per day vs. 5.3 medMJ, 6.2 no-medMJ; p < .01; 57% daily smokers vs. 70% medMJ vs. 71% no-medMJ; p < .01). MJ use was more prevalent in California than other states (59% vs. 53% medMJ, 52% no-medMJ; p < .05). Compared to the other two groups, Californians had less positive expectancies regarding TOB use (p = .03) and anticipated greater success with quitting TOB (p < .01). There were no significant differences on TOB or MJ readiness to quit, % days using both TOB and MJ, abstinence goals, or thoughts about MJ abstinence. Conclusions: Young adult smokers living in California smoked TOB less frequently and intently and anticipated greater success with quitting smoking relative to other states. They were more likely to use MJ but rates of TOB/MJ co-use did not differ. Further, findings suggested comparable thoughts about MJ use and quitting among young adult smokers irrespective of the medical MJ policy in their state. Financial support: TRDRP; 18FT-0055; PI, D. Ramo. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.511